INFORME ANALITICA DE DATOS TPAD-1
TPAD-1
12/2/2021
#INSTALAMOS LAS LIBRERIAS A UTILIZAR
install.packages("hexbin")## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.0'
## (as 'lib' is unspecified)install.packages("readr")## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.0'
## (as 'lib' is unspecified)install.packages("ggplot2")## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.0'
## (as 'lib' is unspecified)install.packages("dplyr")## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.0'
## (as 'lib' is unspecified)install.packages("scatterplot3d",dependencies=T)## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.0'
## (as 'lib' is unspecified)install.packages("tidyverse")## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.0'
## (as 'lib' is unspecified)#CARGAMOS LAS LIBRERIAS QUE VAMOS A UTILIZAR
library(readr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(hexbin)
library(scatterplot3d)
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──## ✓ tibble 3.0.6 ✓ stringr 1.4.0
## ✓ tidyr 1.1.2 ✓ forcats 0.5.1
## ✓ purrr 0.3.4## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()#OBTENEMOS EL ENLACE DE LA CARPETA EN LA QUE ESTAMOS TRABAJANDO
getwd()## [1] "/cloud/project/R-MARKDOWN"#BUSCAMOS LA RUTA DEL DATAFRAME
cafe_df<-read.csv("/cloud/project/R-MARKDOWN/cafesito/produccion.csv", header = TRUE, sep=",", dec=".")#NOS INDICA EL TIPO O CLASE DE LA VARIABLE RENDIMIENTO
class(cafe_df$Rendimiento..ha.ton.)## [1] "numeric"#A CONTINUACION PODEMOS IDENTIFICAR LA ESTRUCTURA DEL DATAFRAME
str(cafe_df)## 'data.frame': 266 obs. of 8 variables:
## $ Anio : int 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 ...
## $ Departamento : chr "ANTIOQUIA" "BOLIVAR" "BOYACA" "CALDAS" ...
## $ Producto : chr "CAFE" "CAFE" "CAFE" "CAFE" ...
## $ Area..ha. : chr "112,343.60" "502" "11,374.50" "78,393.65" ...
## $ Produccion..ton. : chr "120,500.80" "446" "9,683.10" "92,815.00" ...
## $ Rendimiento..ha.ton. : num 1.07 0.89 0.85 1.18 0.93 0.79 0.96 0.57 0.71 0.78 ...
## $ Produccion.Nacional..ton.: num 14.54 0.05 1.17 11.2 0.26 ...
## $ Area.Nacional..ha. : num 14.66 0.07 1.48 10.23 0.3 ...#CONOCER LA DIMENSION DEL DATAFRAME
dim(cafe_df)## [1] 266 8#PODEMOS VER LA INFORMACION RESUMIDA DEL DATAFRAME DE LAS VARIABLES CUANTITATIVAS Y VALORES ESTADISTICOS
summary(cafe_df)## Anio Departamento Producto Area..ha.
## Min. :2007 Length:266 Length:266 Length:266
## 1st Qu.:2010 Class :character Class :character Class :character
## Median :2012 Mode :character Mode :character Mode :character
## Mean :2012
## 3rd Qu.:2015
## Max. :2018
## Produccion..ton. Rendimiento..ha.ton. Produccion.Nacional..ton.
## Length:266 Min. :0.0000 Min. : 0.0000
## Class :character 1st Qu.:0.7500 1st Qu.: 0.3525
## Mode :character Median :0.9400 Median : 2.7200
## Mean :0.9364 Mean : 4.5113
## 3rd Qu.:1.1200 3rd Qu.: 7.1475
## Max. :2.0000 Max. :18.6700
## Area.Nacional..ha.
## Min. : 0.000
## 1st Qu.: 0.390
## Median : 3.120
## Mean : 4.511
## 3rd Qu.: 6.875
## Max. :16.430#INFORMACION CUANTITATIVA DEL DATAFRAME USANDO VARIAS VARIABLES
tapply(cafe_df$Rendimiento..ha.ton., cafe_df$Anio, summary)## $`2007`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.4500 0.7900 0.9000 0.9505 1.1525 1.5300
##
## $`2008`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.4500 0.7775 0.9050 0.9827 1.2000 1.7900
##
## $`2009`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.3000 0.7600 0.9300 0.8814 1.1125 1.2100
##
## $`2010`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.7050 0.9600 0.9061 1.1250 1.5200
##
## $`2011`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.4700 0.6100 0.9000 0.8543 1.0550 1.2000
##
## $`2012`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.7450 0.8300 0.8587 0.9150 2.0000
##
## $`2013`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.6000 0.6000 0.7550 0.7595 0.8800 0.9900
##
## $`2014`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.6400 0.6500 0.8150 0.8223 0.9500 1.0600
##
## $`2015`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.770 0.935 1.065 1.025 1.107 1.150
##
## $`2016`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.790 0.960 1.120 1.064 1.150 1.190
##
## $`2017`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.660 0.845 1.090 1.068 1.290 1.500
##
## $`2018`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.6200 0.8575 1.1200 1.0795 1.3125 1.5200#ALGUNOS VALORES ESTADISTICOS
sum(cafe_df$Rendimiento..ha.ton.)## [1] 249.09prod(cafe_df$Area.Nacional..ha.)## [1] 0var(cafe_df$Rendimiento..ha.ton.)## [1] 0.07135814length(cafe_df$Area.Nacional..ha.)## [1] 266mean(cafe_df$Rendimiento..ha.ton.)## [1] 0.9364286max(cafe_df$Rendimiento..ha.ton.)## [1] 2min(cafe_df$Rendimiento..ha.ton.)## [1] 0sd(cafe_df$Rendimiento..ha.ton.)## [1] 0.2671294#RESTAR EL VALOR MAXIMO Y EL VALOR MINIMO DE LA VARIABLE RENDIMIENTO
max(cafe_df$Rendimiento..ha.ton.)-min(cafe_df$Rendimiento..ha.ton.)## [1] 2#NOMBRE DE LAS VARIABLES DEL DATAFRAME
names(cafe_df)## [1] "Anio" "Departamento"
## [3] "Producto" "Area..ha."
## [5] "Produccion..ton." "Rendimiento..ha.ton."
## [7] "Produccion.Nacional..ton." "Area.Nacional..ha."colnames(cafe_df)## [1] "Anio" "Departamento"
## [3] "Producto" "Area..ha."
## [5] "Produccion..ton." "Rendimiento..ha.ton."
## [7] "Produccion.Nacional..ton." "Area.Nacional..ha."#VER LAS PRIMERAS Y ULTIMAS 6 FILAS DEL DATAFRAME
head(cafe_df)## Anio Departamento Producto Area..ha. Produccion..ton. Rendimiento..ha.ton.
## 1 2007 ANTIOQUIA CAFE 112,343.60 120,500.80 1.07
## 2 2007 BOLIVAR CAFE 502 446 0.89
## 3 2007 BOYACA CAFE 11,374.50 9,683.10 0.85
## 4 2007 CALDAS CAFE 78,393.65 92,815.00 1.18
## 5 2007 CAQUETA CAFE 2,295.00 2,134.00 0.93
## 6 2007 CASANARE CAFE 2,605.00 2,048.40 0.79
## Produccion.Nacional..ton. Area.Nacional..ha.
## 1 14.54 14.66
## 2 0.05 0.07
## 3 1.17 1.48
## 4 11.20 10.23
## 5 0.26 0.30
## 6 0.25 0.34tail(cafe_df)## Anio Departamento Producto Area..ha. Produccion..ton.
## 261 2018 PUTUMAYO CAFE 209.93 289.5
## 262 2018 QUINDIO CAFE 16,374.73 17,739.03
## 263 2018 RISARALDA CAFE 35,874.73 45,918.75
## 264 2018 SANTANDER CAFE 42,269.07 55,918.71
## 265 2018 TOLIMA CAFE 97,304.04 97,451.31
## 266 2018 VALLE DEL CAUCA CAFE 48,305.31 49,667.88
## Rendimiento..ha.ton. Produccion.Nacional..ton. Area.Nacional..ha.
## 261 1.38 0.03 0.03
## 262 1.08 2.07 2.21
## 263 1.28 5.37 4.83
## 264 1.32 6.53 5.69
## 265 1.00 11.39 13.11
## 266 1.03 5.80 6.51#INSPECCIONAR DE FORMA DETALLADA EN EL DATAFRAME SELECCIONANDO SUS COLUMNAS Y FILAS
cafe_df[3]## Producto
## 1 CAFE
## 2 CAFE
## 3 CAFE
## 4 CAFE
## 5 CAFE
## 6 CAFE
## 7 CAFE
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## 242 CAFE
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## 256 CAFE
## 257 CAFE
## 258 CAFE
## 259 CAFE
## 260 CAFE
## 261 CAFE
## 262 CAFE
## 263 CAFE
## 264 CAFE
## 265 CAFE
## 266 CAFEcafe_df[,4]## [1] "112,343.60" "502" "11,374.50" "78,393.65" "2,295.00"
## [6] "2,605.00" "53,471.00" "23,172.00" "290" "43,017.30"
## [11] "89,661.56" "4,785.00" "17,506.00" "2,048.00" "24,458.50"
## [16] "30,171.84" "35" "19,904.00" "47,689.25" "34,406.67"
## [21] "91,679.10" "76,667.80" "114,694.00" "572" "10,778.50"
## [26] "74,897.00" "2,735.00" "2,149.00" "56,208.00" "23,198.00"
## [31] "90" "43,633.35" "89,131.20" "4,553.00" "17,521.00"
## [36] "2,146.00" "25,582.00" "30,171.84" "31" "19,571.00"
## [41] "47,227.00" "34,169.37" "86,829.20" "72,419.00" "112,420.20"
## [46] "770" "10,672.50" "73,083.00" "2,332.00" "1,904.00"
## [51] "57,860.00" "23,420.00" "70" "43,475.84" "86,726.78"
## [56] "4,488.00" "17,036.00" "2,216.00" "26,467.20" "33,552.58"
## [61] "23" "19,052.00" "45,428.00" "37,985.90" "88,667.00"
## [66] "67,001.30" "111,602.71" "0" "850" "9,427.00"
## [71] "72,240.58" "2,536.00" "2,198.00" "55,162.00" "22,489.50"
## [76] "157.5" "44,264.16" "87,139.53" "4,207.00" "17,000.00"
## [81] "2,326.00" "23,504.05" "30,731.96" "24" "18,159.00"
## [86] "47,308.00" "39,000.64" "84,658.70" "69,332.10" "106,419.57"
## [91] "10" "850" "8,441.74" "66,331.61" "2,810.00"
## [96] "2,081.50" "54,246.42" "22,350.00" "157.5" "37,478.87"
## [101] "78,792.21" "4,100.00" "16,577.00" "2,578.00" "24,263.80"
## [106] "21,520.45" "40" "20,139.30" "44,733.64" "37,282.04"
## [111] "93,145.35" "68,038.40" "112,221.14" "870" "6,698.20"
## [116] "54,871.88" "2,882.50" "2,322.00" "56,825.00" "22,911.00"
## [121] "70" "37,175.06" "0" "79,809.34" "5,143.00"
## [126] "17,686.00" "2,783.00" "27,806.40" "19,339.31" "42"
## [131] "21,109.83" "45,588.03" "33,947.15" "90,904.48" "69,456.71"
## [136] "109,755.50" "659.04" "9,289.05" "60,264.29" "2,905.84"
## [141] "2,232.94" "74,105.64" "25,106.39" "125.01" "36,189.18"
## [146] "118,200.88" "5,750.70" "17,016.72" "2,483.43" "32,136.51"
## [151] "25,332.45" "24.27" "21,203.03" "39,615.60" "38,613.68"
## [156] "97,308.81" "53,481.02" "110,115.86" "936.34" "9,834.39"
## [161] "59,757.18" "3,074.92" "2,599.43" "77,068.46" "26,138.58"
## [166] "136.88" "33,623.54" "128,273.15" "6,078.64" "18,533.11"
## [171] "2,739.71" "33,608.32" "23,724.20" "101.16" "21,462.81"
## [176] "40,154.46" "40,733.20" "100,832.91" "56,035.94" "109,649.61"
## [181] "1,065.07" "10,461.85" "58,376.40" "3,410.56" "2,752.31"
## [186] "77,405.83" "25,948.50" "137.47" "34,101.49" "130,452.40"
## [191] "5,631.53" "17,996.31" "2,922.21" "33,490.93" "22,940.64"
## [196] "128.65" "21,491.21" "41,732.03" "42,679.11" "103,368.73"
## [201] "54,938.79" "105,666.60" "1,065.97" "10,181.80" "56,022.04"
## [206] "3,392.22" "2,671.04" "78,421.95" "25,530.59" "134.96"
## [211] "33,214.17" "126,052.15" "5,531.20" "17,745.80" "2,924.89"
## [216] "32,750.16" "21,520.64" "20,041.70" "40,472.26" "41,387.79"
## [221] "100,328.77" "52,648.25" "99,311.53" "1,137.42" "9,598.33"
## [226] "51,854.59" "3,408.69" "2,436.63" "80,289.56" "25,158.80"
## [231] "125.67" "30,894.16" "122,575.76" "5,340.80" "18,129.50"
## [236] "2,926.85" "33,639.55" "21,409.77" "209.29" "17,699.67"
## [241] "37,334.16" "42,327.26" "96,018.89" "51,470.86" "98,038.15"
## [246] "1,182.13" "9,653.45" "50,762.22" "3,485.24" "2,360.55"
## [251] "82,085.54" "23,915.45" "140.33" "29,085.24" "122,002.46"
## [256] "4,810.97" "17,414.32" "2,761.01" "33,465.54" "20,873.04"
## [261] "209.93" "16,374.73" "35,874.73" "42,269.07" "97,304.04"
## [266] "48,305.31"cafe_df[,2]## [1] "ANTIOQUIA" "BOLIVAR" "BOYACA"
## [4] "CALDAS" "CAQUETA" "CASANARE"
## [7] "CAUCA" "CESAR" "CHOCO"
## [10] "CUNDINAMARCA" "HUILA" "LA GUAJIRA"
## [13] "MAGDALENA" "META" "NARIÑO"
## [16] "NORTE DE SANTANDER" "PUTUMAYO" "QUINDIO"
## [19] "RISARALDA" "SANTANDER" "TOLIMA"
## [22] "VALLE DEL CAUCA" "ANTIOQUIA" "BOLIVAR"
## [25] "BOYACA" "CALDAS" "CAQUETA"
## [28] "CASANARE" "CAUCA" "CESAR"
## [31] "CHOCO" "CUNDINAMARCA" "HUILA"
## [34] "LA GUAJIRA" "MAGDALENA" "META"
## [37] "NARIÑO" "NORTE DE SANTANDER" "PUTUMAYO"
## [40] "QUINDIO" "RISARALDA" "SANTANDER"
## [43] "TOLIMA" "VALLE DEL CAUCA" "ANTIOQUIA"
## [46] "BOLIVAR" "BOYACA" "CALDAS"
## [49] "CAQUETA" "CASANARE" "CAUCA"
## [52] "CESAR" "CHOCO" "CUNDINAMARCA"
## [55] "HUILA" "LA GUAJIRA" "MAGDALENA"
## [58] "META" "NARIÑO" "NORTE DE SANTANDER"
## [61] "PUTUMAYO" "QUINDIO" "RISARALDA"
## [64] "SANTANDER" "TOLIMA" "VALLE DEL CAUCA"
## [67] "ANTIOQUIA" "ARAUCA" "BOLIVAR"
## [70] "BOYACA" "CALDAS" "CAQUETA"
## [73] "CASANARE" "CAUCA" "CESAR"
## [76] "CHOCO" "CUNDINAMARCA" "HUILA"
## [79] "LA GUAJIRA" "MAGDALENA" "META"
## [82] "NARIÑO" "NORTE DE SANTANDER" "PUTUMAYO"
## [85] "QUINDIO" "RISARALDA" "SANTANDER"
## [88] "TOLIMA" "VALLE DEL CAUCA" "ANTIOQUIA"
## [91] "ARAUCA" "BOLIVAR" "BOYACA"
## [94] "CALDAS" "CAQUETA" "CASANARE"
## [97] "CAUCA" "CESAR" "CHOCO"
## [100] "CUNDINAMARCA" "HUILA" "LA GUAJIRA"
## [103] "MAGDALENA" "META" "NARIÑO"
## [106] "NORTE DE SANTANDER" "PUTUMAYO" "QUINDIO"
## [109] "RISARALDA" "SANTANDER" "TOLIMA"
## [112] "VALLE DEL CAUCA" "ANTIOQUIA" "BOLIVAR"
## [115] "BOYACA" "CALDAS" "CAQUETA"
## [118] "CASANARE" "CAUCA" "CESAR"
## [121] "CHOCO" "CUNDINAMARCA" "GUAVIARE"
## [124] "HUILA" "LA GUAJIRA" "MAGDALENA"
## [127] "META" "NARIÑO" "NORTE DE SANTANDER"
## [130] "PUTUMAYO" "QUINDIO" "RISARALDA"
## [133] "SANTANDER" "TOLIMA" "VALLE DEL CAUCA"
## [136] "ANTIOQUIA" "BOLIVAR" "BOYACA"
## [139] "CALDAS" "CAQUETA" "CASANARE"
## [142] "CAUCA" "CESAR" "CHOCO"
## [145] "CUNDINAMARCA" "HUILA" "LA GUAJIRA"
## [148] "MAGDALENA" "META" "NARIÑO"
## [151] "NORTE DE SANTANDER" "PUTUMAYO" "QUINDIO"
## [154] "RISARALDA" "SANTANDER" "TOLIMA"
## [157] "VALLE DEL CAUCA" "ANTIOQUIA" "BOLIVAR"
## [160] "BOYACA" "CALDAS" "CAQUETA"
## [163] "CASANARE" "CAUCA" "CESAR"
## [166] "CHOCO" "CUNDINAMARCA" "HUILA"
## [169] "LA GUAJIRA" "MAGDALENA" "META"
## [172] "NARIÑO" "NORTE DE SANTANDER" "PUTUMAYO"
## [175] "QUINDIO" "RISARALDA" "SANTANDER"
## [178] "TOLIMA" "VALLE DEL CAUCA" "ANTIOQUIA"
## [181] "BOLIVAR" "BOYACA" "CALDAS"
## [184] "CAQUETA" "CASANARE" "CAUCA"
## [187] "CESAR" "CHOCO" "CUNDINAMARCA"
## [190] "HUILA" "LA GUAJIRA" "MAGDALENA"
## [193] "META" "NARIÑO" "NORTE DE SANTANDER"
## [196] "PUTUMAYO" "QUINDIO" "RISARALDA"
## [199] "SANTANDER" "TOLIMA" "VALLE DEL CAUCA"
## [202] "ANTIOQUIA" "BOLIVAR" "BOYACA"
## [205] "CALDAS" "CAQUETA" "CASANARE"
## [208] "CAUCA" "CESAR" "CHOCO"
## [211] "CUNDINAMARCA" "HUILA" "LA GUAJIRA"
## [214] "MAGDALENA" "META" "NARIÑO"
## [217] "NORTE DE SANTANDER" "QUINDIO" "RISARALDA"
## [220] "SANTANDER" "TOLIMA" "VALLE DEL CAUCA"
## [223] "ANTIOQUIA" "BOLIVAR" "BOYACA"
## [226] "CALDAS" "CAQUETA" "CASANARE"
## [229] "CAUCA" "CESAR" "CHOCO"
## [232] "CUNDINAMARCA" "HUILA" "LA GUAJIRA"
## [235] "MAGDALENA" "META" "NARIÑO"
## [238] "NORTE DE SANTANDER" "PUTUMAYO" "QUINDIO"
## [241] "RISARALDA" "SANTANDER" "TOLIMA"
## [244] "VALLE DEL CAUCA" "ANTIOQUIA" "BOLIVAR"
## [247] "BOYACA" "CALDAS" "CAQUETA"
## [250] "CASANARE" "CAUCA" "CESAR"
## [253] "CHOCO" "CUNDINAMARCA" "HUILA"
## [256] "LA GUAJIRA" "MAGDALENA" "META"
## [259] "NARIÑO" "NORTE DE SANTANDER" "PUTUMAYO"
## [262] "QUINDIO" "RISARALDA" "SANTANDER"
## [265] "TOLIMA" "VALLE DEL CAUCA"cafe_df[2,]## Anio Departamento Producto Area..ha. Produccion..ton. Rendimiento..ha.ton.
## 2 2007 BOLIVAR CAFE 502 446 0.89
## Produccion.Nacional..ton. Area.Nacional..ha.
## 2 0.05 0.07cafe_df[100,]## Anio Departamento Producto Area..ha. Produccion..ton. Rendimiento..ha.ton.
## 100 2011 CUNDINAMARCA CAFE 37,478.87 32,780.35 0.87
## Produccion.Nacional..ton. Area.Nacional..ha.
## 100 5.12 5.26#CON LA FUNCION TABLE LISTAMOS LAS FILAS DE LA VARIABLE SELECCIONADA
table(cafe_df$Produccion..ton.)##
## 0 1,089.74 1,128.32 1,338.56 1,388.13 1,617.20 1,629.25
## 2 1 1 1 1 1 1
## 1,650.41 1,656.96 1,672.60 1,688.60 1,718.25 1,747.51 1,933.00
## 1 1 1 1 1 1 1
## 1,950.84 10,200.84 10,221.69 10,826.24 101,201.88 102,147.00 102,403.24
## 1 1 1 1 1 1 1
## 103,703.00 104,336.56 104,609.42 105,563.88 105,976.19 105.93 11,035.85
## 1 1 1 1 1 1 1
## 11,937.90 111,452.91 112,322.38 113,505.20 115,267.98 115,874.98 119,970.68
## 1 1 1 1 1 1 1
## 12 12,012.98 12,214.54 12,332.00 12,770.00 120,365.77 120,500.80
## 1 1 1 1 1 1 1
## 121,253.38 124.67 125.42 129,052.51 13,276.08 13,278.50 13,301.60
## 1 1 1 1 1 1 1
## 13,412.80 13,593.24 13,593.25 13,600.00 13,841.45 131,316.47 133,787.95
## 1 1 1 1 1 1 1
## 135,971.20 136,161.86 14,005.00 14,017.00 14,096.05 14,943.62 140
## 1 1 1 1 1 1 1
## 140,398.62 141,898.91 145,154.42 145,168.10 15,050.27 15,108.55 15,185.79
## 1 1 1 1 1 1 1
## 158.2 158.85 16,628.14 16,691.31 16,935.63 16.87 160.62
## 1 1 1 1 1 1 1
## 17,031.09 17,739.03 18,030.13 18,792.05 181.42 19,590.10 19,994.35
## 1 1 1 1 1 1 1
## 2,023.50 2,048.40 2,079.70 2,133.10 2,134.00 2,188.92 2,221.90
## 1 1 1 1 1 1 1
## 2,328.90 2,332.00 2,340.40 2,393.00 2,446.38 2,469.00 2,503.81
## 1 1 1 1 1 1 1
## 2,528.40 2,533.75 2,564.86 2,626.73 2,638.88 2,902.50 2,958.70
## 1 1 1 1 1 1 1
## 2,990.91 20,267.64 20,599.27 20,814.11 205.9 21,065.00 21,985.00
## 1 1 1 1 1 1 1
## 22,089.82 22,111.65 22,240.81 22,518.42 22,649.03 23,271.89 23,409.44
## 1 1 1 1 1 1 1
## 23,471.69 23,669.00 23,791.30 24,073.95 24,594.10 24,694.56 24,993.74
## 1 1 1 1 1 1 1
## 25,118.55 25,426.00 26,311.61 26.7 27,094.16 27,487.71 28,077.94
## 1 1 1 2 1 1 1
## 28,606.96 282.18 289.5 29,016.75 29,469.52 292.6 3,206.35
## 1 1 1 1 1 1 1
## 3,322.42 3,434.30 3,447.31 3,516.80 3,749.27 3,861.63 3,877.62
## 1 1 1 1 1 1 1
## 3,923.80 30,227.02 30,786.41 31,165.15 31,262.50 31,413.34 31,770.05
## 1 1 1 1 1 1 1
## 32,321.56 32,580.24 32,780.35 33,729.14 33,943.39 34 34,512.79
## 1 1 1 1 1 1 1
## 35,004.18 35,679.42 35.6 36,607.56 36,989.43 37,020.90 37,118.07
## 1 1 1 1 1 1 1
## 37,214.80 39,073.92 395.07 4,013.11 4,317.50 4,387.19 4,981.59
## 1 1 1 1 1 1 1
## 41,645.39 42,719.53 42,948.40 446 45,113.00 45,918.75 45.8
## 1 1 1 1 1 1 1
## 46,779.71 47,215.69 47,221.00 47,304.16 47,357.02 47,512.36 48,073.00
## 1 1 1 1 1 1 1
## 48.4 49,042.31 49,667.88 49,799.28 5,108.33 5,280.40 5,591.05
## 1 1 1 1 1 1 1
## 5,643.39 50,588.14 51,348.00 51,687.80 510 53,288.42 53,648.00
## 1 1 1 1 2 1 1
## 54,115.96 54,908.68 55,918.71 56,303.92 57,067.08 57,583.56 58,634.19
## 1 1 1 1 1 1 1
## 6,364.41 60,079.00 606.93 61,190.55 62,711.08 62,869.38 63,365.76
## 1 1 1 1 1 1 1
## 65,475.63 65,666.43 652.5 66,661.14 67,231.37 68 68,668.20
## 1 1 1 1 1 1 1
## 68,670.96 69,496.65 69,618.24 7,083.07 7,638.99 7,780.34 711
## 1 1 1 1 1 1 1
## 72,091.00 72,842.55 734.91 748.97 76.04 77,215.36 78,254.77
## 1 1 1 1 1 1 1
## 78,805.87 78.75 8,567.97 81,668.22 83,626.44 85,027.49 85,150.66
## 1 1 1 1 1 1 1
## 85,212.64 86,453.62 86,884.00 87,642.49 88,633.10 9,501.54 9,547.30
## 1 1 1 1 1 1 1
## 9,583.80 9,683.10 91,621.30 92,815.00 94,230.20 94,556.71 95,957.90
## 1 1 1 1 1 1 1
## 97,451.31 97,922.49 98
## 1 1 2table(cafe_df$Rendimiento..ha.ton.)##
## 0 0.3 0.38 0.45 0.47 0.49 0.51 0.52 0.55 0.57 0.59 0.6 0.62 0.63 0.64 0.65
## 2 1 1 2 1 1 1 1 1 4 2 10 7 1 1 7
## 0.66 0.67 0.69 0.7 0.71 0.72 0.74 0.75 0.76 0.77 0.78 0.79 0.8 0.81 0.82 0.83
## 5 2 5 1 2 2 2 6 2 4 2 5 8 3 4 1
## 0.84 0.85 0.86 0.87 0.88 0.89 0.9 0.91 0.92 0.93 0.94 0.95 0.96 0.97 0.98 0.99
## 1 7 4 2 2 6 2 5 2 3 3 2 5 5 3 5
## 1 1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08 1.09 1.1 1.11 1.12 1.13 1.14 1.15
## 5 2 2 2 2 4 5 3 5 5 5 4 5 3 4 9
## 1.16 1.17 1.18 1.19 1.2 1.21 1.22 1.23 1.24 1.25 1.26 1.27 1.28 1.29 1.3 1.32
## 3 3 2 4 2 2 2 1 2 1 1 1 2 1 2 2
## 1.33 1.35 1.37 1.38 1.4 1.41 1.44 1.45 1.47 1.5 1.52 1.53 1.79 2
## 1 2 1 1 1 1 1 1 1 1 2 1 1 1#PODEMOS VER LA UTILIDAD E INFORMACION DE LA FUNCION
help (subset)#CON LA FUNCION SUBSET PODEMOS SELECCIONAR POR VARIABLES EN NUESTRO DATAFRAME
subset(cafe_df, subset = Departamento == 'HUILA')## Anio Departamento Producto Area..ha. Produccion..ton. Rendimiento..ha.ton.
## 11 2007 HUILA CAFE 89,661.56 129,052.51 1.44
## 33 2008 HUILA CAFE 89,131.20 131,316.47 1.47
## 55 2009 HUILA CAFE 86,726.78 104,609.42 1.21
## 78 2010 HUILA CAFE 87,139.53 104,336.56 1.20
## 101 2011 HUILA CAFE 78,792.21 85,150.66 1.08
## 124 2012 HUILA CAFE 79,809.34 85,212.64 1.07
## 146 2013 HUILA CAFE 118,200.88 115,874.98 0.98
## 168 2014 HUILA CAFE 128,273.15 135,971.20 1.06
## 190 2015 HUILA CAFE 130,452.40 145,168.10 1.11
## 212 2016 HUILA CAFE 126,052.15 145,154.42 1.15
## 233 2017 HUILA CAFE 122,575.76 133,787.95 1.09
## 255 2018 HUILA CAFE 122,002.46 136,161.86 1.12
## Produccion.Nacional..ton. Area.Nacional..ha.
## 11 15.57 11.70
## 33 15.85 11.75
## 55 14.76 11.49
## 78 13.39 11.71
## 101 13.30 11.06
## 124 13.60 11.23
## 146 17.77 15.31
## 168 18.67 16.12
## 190 17.07 16.28
## 212 17.00 16.21
## 233 15.71 16.27
## 255 15.91 16.43subset(cafe_df, subset = Departamento == 'META')## Anio Departamento Producto Area..ha. Produccion..ton. Rendimiento..ha.ton.
## 14 2007 META CAFE 2,048.00 1,617.20 0.79
## 36 2008 META CAFE 2,146.00 1,656.96 0.77
## 58 2009 META CAFE 2,216.00 1,672.60 0.75
## 81 2010 META CAFE 2,326.00 2,221.90 0.96
## 104 2011 META CAFE 2,578.00 2,533.75 0.98
## 127 2012 META CAFE 2,783.00 2,133.10 0.77
## 149 2013 META CAFE 2,483.43 1,650.41 0.66
## 171 2014 META CAFE 2,739.71 1,950.84 0.71
## 193 2015 META CAFE 2,922.21 3,206.35 1.10
## 215 2016 META CAFE 2,924.89 3,322.42 1.14
## 236 2017 META CAFE 2,926.85 4,013.11 1.37
## 258 2018 META CAFE 2,761.01 3,877.62 1.40
## Produccion.Nacional..ton. Area.Nacional..ha.
## 14 0.20 0.27
## 36 0.20 0.28
## 58 0.24 0.29
## 81 0.29 0.31
## 104 0.40 0.36
## 127 0.34 0.39
## 149 0.25 0.32
## 171 0.27 0.34
## 193 0.38 0.36
## 215 0.39 0.38
## 236 0.47 0.39
## 258 0.45 0.37subset(cafe_df, subset = Anio == '2009')## Anio Departamento Producto Area..ha. Produccion..ton.
## 45 2009 ANTIOQUIA CAFE 112,420.20 103,703.00
## 46 2009 BOLIVAR CAFE 770 292.6
## 47 2009 BOYACA CAFE 10,672.50 8,567.97
## 48 2009 CALDAS CAFE 73,083.00 81,668.22
## 49 2009 CAQUETA CAFE 2,332.00 2,332.00
## 50 2009 CASANARE CAFE 1,904.00 2,079.70
## 51 2009 CAUCA CAFE 57,860.00 47,221.00
## 52 2009 CESAR CAFE 23,420.00 12,770.00
## 53 2009 CHOCO CAFE 70 78.75
## 54 2009 CUNDINAMARCA CAFE 43,475.84 37,118.07
## 55 2009 HUILA CAFE 86,726.78 104,609.42
## 56 2009 LA GUAJIRA CAFE 4,488.00 2,340.40
## 57 2009 MAGDALENA CAFE 17,036.00 13,412.80
## 58 2009 META CAFE 2,216.00 1,672.60
## 59 2009 NARIÑO CAFE 26,467.20 27,487.71
## 60 2009 NORTE DE SANTANDER CAFE 33,552.58 10,221.69
## 61 2009 PUTUMAYO CAFE 23 26.7
## 62 2009 QUINDIO CAFE 19,052.00 21,985.00
## 63 2009 RISARALDA CAFE 45,428.00 53,648.00
## 64 2009 SANTANDER CAFE 37,985.90 26,311.61
## 65 2009 TOLIMA CAFE 88,667.00 88,633.10
## 66 2009 VALLE DEL CAUCA CAFE 67,001.30 62,711.08
## Rendimiento..ha.ton. Produccion.Nacional..ton. Area.Nacional..ha.
## 45 0.92 14.63 14.90
## 46 0.38 0.04 0.10
## 47 0.80 1.21 1.41
## 48 1.12 11.52 9.68
## 49 1.00 0.33 0.31
## 50 1.09 0.29 0.25
## 51 0.82 6.66 7.67
## 52 0.55 1.80 3.10
## 53 1.13 0.01 0.01
## 54 0.85 5.24 5.76
## 55 1.21 14.76 11.49
## 56 0.52 0.33 0.59
## 57 0.79 1.89 2.26
## 58 0.75 0.24 0.29
## 59 1.04 3.88 3.51
## 60 0.30 1.44 4.45
## 61 1.16 0.00 0.00
## 62 1.15 3.10 2.52
## 63 1.18 7.57 6.02
## 64 0.69 3.71 5.03
## 65 1.00 12.50 11.75
## 66 0.94 8.85 8.88subset(cafe_df, subset = Anio == '2012')## Anio Departamento Producto Area..ha. Produccion..ton.
## 113 2012 ANTIOQUIA CAFE 112,221.14 91,621.30
## 114 2012 BOLIVAR CAFE 870 652.5
## 115 2012 BOYACA CAFE 6,698.20 4,981.59
## 116 2012 CALDAS CAFE 54,871.88 54,115.96
## 117 2012 CAQUETA CAFE 2,882.50 2,446.38
## 118 2012 CASANARE CAFE 2,322.00 1,718.25
## 119 2012 CAUCA CAFE 56,825.00 50,588.14
## 120 2012 CESAR CAFE 22,911.00 19,994.35
## 121 2012 CHOCO CAFE 70 140
## 122 2012 CUNDINAMARCA CAFE 37,175.06 30,786.41
## 123 2012 GUAVIARE CAFE 0 0
## 124 2012 HUILA CAFE 79,809.34 85,212.64
## 125 2012 LA GUAJIRA CAFE 5,143.00 3,434.30
## 126 2012 MAGDALENA CAFE 17,686.00 14,096.05
## 127 2012 META CAFE 2,783.00 2,133.10
## 128 2012 NARIÑO CAFE 27,806.40 28,077.94
## 129 2012 NORTE DE SANTANDER CAFE 19,339.31 12,214.54
## 130 2012 PUTUMAYO CAFE 42 48.4
## 131 2012 QUINDIO CAFE 21,109.83 18,030.13
## 132 2012 RISARALDA CAFE 45,588.03 36,989.43
## 133 2012 SANTANDER CAFE 33,947.15 23,271.89
## 134 2012 TOLIMA CAFE 90,904.48 85,027.49
## 135 2012 VALLE DEL CAUCA CAFE 69,456.71 61,190.55
## Rendimiento..ha.ton. Produccion.Nacional..ton. Area.Nacional..ha.
## 113 0.82 14.62 15.80
## 114 0.75 0.10 0.12
## 115 0.74 0.79 0.94
## 116 0.99 8.63 7.72
## 117 0.85 0.39 0.41
## 118 0.74 0.27 0.33
## 119 0.89 8.07 8.00
## 120 0.87 3.19 3.22
## 121 2.00 0.02 0.01
## 122 0.83 4.91 5.23
## 123 0.00 0.00 0.00
## 124 1.07 13.60 11.23
## 125 0.67 0.55 0.72
## 126 0.80 2.25 2.49
## 127 0.77 0.34 0.39
## 128 1.01 4.48 3.91
## 129 0.63 1.95 2.72
## 130 1.15 0.01 0.01
## 131 0.85 2.88 2.97
## 132 0.81 5.90 6.42
## 133 0.69 3.71 4.78
## 134 0.94 13.57 12.80
## 135 0.88 9.76 9.78subset(cafe_df, subset=Anio == '2009',
select=c('Rendimiento..ha.ton.', 'Produccion.Nacional..ton.'))## Rendimiento..ha.ton. Produccion.Nacional..ton.
## 45 0.92 14.63
## 46 0.38 0.04
## 47 0.80 1.21
## 48 1.12 11.52
## 49 1.00 0.33
## 50 1.09 0.29
## 51 0.82 6.66
## 52 0.55 1.80
## 53 1.13 0.01
## 54 0.85 5.24
## 55 1.21 14.76
## 56 0.52 0.33
## 57 0.79 1.89
## 58 0.75 0.24
## 59 1.04 3.88
## 60 0.30 1.44
## 61 1.16 0.00
## 62 1.15 3.10
## 63 1.18 7.57
## 64 0.69 3.71
## 65 1.00 12.50
## 66 0.94 8.85subset(cafe_df, subset=Departamento == 'CUNDINAMARCA',
select=c('Rendimiento..ha.ton.', 'Produccion.Nacional..ton.'))## Rendimiento..ha.ton. Produccion.Nacional..ton.
## 10 0.78 4.07
## 32 1.79 9.44
## 54 0.85 5.24
## 77 0.84 4.78
## 100 0.87 5.12
## 122 0.83 4.91
## 145 0.69 3.83
## 167 0.75 3.45
## 189 0.91 3.66
## 211 0.95 3.68
## 232 1.10 3.99
## 254 1.12 3.81subset(cafe_df, subset=Departamento == 'CUNDINAMARCA' & Anio <= '2012',
select=c( 'Produccion.Nacional..ton.', 'Area.Nacional..ha.', 'Rendimiento..ha.ton.'))## Produccion.Nacional..ton. Area.Nacional..ha. Rendimiento..ha.ton.
## 10 4.07 5.61 0.78
## 32 9.44 5.75 1.79
## 54 5.24 5.76 0.85
## 77 4.78 5.95 0.84
## 100 5.12 5.26 0.87
## 122 4.91 5.23 0.83#PONEMOS LA INSTRUCCION EN UNA VARIABLE Y LO GRAFICAMOS
Grupo_Antioquia<-subset(cafe_df, subset=Departamento == 'ANTIOQUIA' & Anio <= '2012',
select=c( 'Produccion.Nacional..ton.', 'Area.Nacional..ha.', 'Rendimiento..ha.ton.'))
Grupo_Antioquia## Produccion.Nacional..ton. Area.Nacional..ha. Rendimiento..ha.ton.
## 1 14.54 14.66 1.07
## 23 13.70 15.13 0.99
## 45 14.63 14.90 0.92
## 67 15.56 14.99 1.09
## 90 18.00 14.94 1.08
## 113 14.62 15.80 0.82plot(Grupo_Antioquia)
#PODEMOS VER SI EXISTEN DATOS NULOS EN EL DATAFRAME
is.na(cafe_df)## Anio Departamento Producto Area..ha. Produccion..ton.
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## [265] FALSE FALSEis.na(cafe_df$Rendimiento..ha.ton.)## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## [265] FALSE FALSE#NOS MUESTRA LA SUMA DE LOS VALORES NULOS EN EL DATAFRAME
rowSums(is.na(cafe_df))## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [75] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [112] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [149] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [186] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [223] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [260] 0 0 0 0 0 0 0#NOS MUESTRA LA SUMA TOTAL DE TODOS LOS VALORES NULOS
sum(rowSums(is.na(cafe_df)))## [1] 0#VISUALIZAMOS HISTOGRAMAS POR VARIABLE
plot(cafe_df$Rendimiento..ha.ton.)
#GRAFICAMOS LA VARIABLE Y SELECCIONAMOS ALGUNOS DETALLES
plot(cafe_df$Rendimiento..ha.ton., main ="Gráfico Dispersion",
sub ="CANTIDAD",
type ="p", # p indica puntos de dispersion
col ="red",
xlab ="cantidad",
ylab = "Rendimiento (ha/ton)")
plot(cafe_df$Rendimiento..ha.ton., main ="Gráfico Dispersion",
sub ="Indices",
type ="b", # b indica lineas uniendo los puntos de dispersion
col ="red",
xlab ="cantidad",
ylab = "Rendimiento (ha/ton)")
plot(cafe_df$Rendimiento..ha.ton., main ="Gráfico Dispersion",
sub ="Indices",
type ="o", # b indica lineas sobreindicadas sobre los puntos de dispersion
col ="blue",
xlab ="cantidad",
ylab = "Rendimiento (ha/ton)")
plot(cafe_df$Rendimiento..ha.ton., main ="Gráfico Dispersion",
sub ="Indices",
type ="h", # b indica lineas tipo histograma sobre los puntos de dispersion
col ="blue",
xlab ="cantidad",
ylab = "Rendimiento (ha/ton)")
plot(cafe_df$Rendimiento..ha.ton., main ="Gráfico Dispersion",
sub ="Indices",
type ="s", # s funcion escalera (horizontal a vertical)
col ="blue",
xlab ="cantidad",
ylab = "Rendimiento (ha/ton)")
plot(cafe_df$Rendimiento..ha.ton., main ="Gráfico Dispersion",
sub ="Indices",
type ="l", # l indica lineas
col ="blue",
xlab ="cantidad",
ylab = "Rendimiento (ha/ton)")
#GRAFICAS POR VARIABLES
plot(x = cafe_df$Area..ha., y = cafe_df$Rendimiento..ha.ton.)## Warning in xy.coords(x, y, xlabel, ylabel, log): NAs introduced by coercion
plot(x = cafe_df$Area..ha., y = cafe_df$Area.Nacional..ha.)## Warning in xy.coords(x, y, xlabel, ylabel, log): NAs introduced by coercion
plot(x = cafe_df$Anio, y = cafe_df$Rendimiento..ha.ton.)
plot(x = cafe_df$Anio, y = cafe_df$Produccion..ton.)## Warning in xy.coords(x, y, xlabel, ylabel, log): NAs introduced by coercion
plot(x = cafe_df$Area..ha., y = cafe_df$Produccion.Nacional..ton.)## Warning in xy.coords(x, y, xlabel, ylabel, log): NAs introduced by coercion
plot(cafe_df$Area..ha.)## Warning in xy.coords(x, y, xlabel, ylabel, log): NAs introduced by coercion
#REPRESENTA LA FRECUENCIA DE LOS DATOS EN UN HISTOGRAMA
hist(cafe_df$Rendimiento..ha.ton., main ="Histograma",
sub ="Rendimiento (ha/ton)",
col ="red",
xlab ="Cantidad (miles) ",
ylab = "Frecuencia")
#DIAGRAMA DE BARRAS DE LA COLUMNA RENDIMIENTO
barplot(table(cafe_df$Rendimiento..ha.ton.))
#PARA VER COMO SE DISTRUBUYEN LOS DATOS PODEMOS USAR LA FUNCION DENSITY, LA DENSIDAD ES UNA VERSION SUAVIZASA DEL HISTOGRAMA, NOS PERMITE OBSERVAR SI LOS DATOS SE COMPORTAN COMO UNA DISTRIBUCION NORMAL
plot(density(cafe_df$Rendimiento..ha.ton.), main="Densidad para el Rendimiento (ha/ton)")
plot(density(cafe_df$Produccion.Nacional..ton.), main="Densidad para la produccion Nacional (ton)")
plot(density(cafe_df$Area.Nacional..ha.), main="Densidad para el Area Nacional (ha)")
#HACEMOS UN GRAFICO DE PASTEL
pie(table(cafe_df$Departamento[1:10]))
pie(table(cafe_df$Rendimiento..ha.ton.[1:10]))
#LOS BOXPLOTS O DIAGRAMAS DE CAJA SE CONTRIBUYEN A PARTIR DE LOS PERCENTILES. #SE CONSTRUYE UN RECTANGULO USANDO ENTRE EL PRIMER Y EL TERCER CUARTIL. #LA ALTURA DEL RECTANGULO ES EL RANGO INTERCUARTIL. #LA MEDIANA ES UNA LINEA QUE DIVIDE EL RECTANGULO- #LOS VALORES MAS EXTREMOS QUE EL LARGO DE LOS BRAZOS SON CONSIDERADOS ATIPICOS #EL BOXPLOT NOS ENTREGA INFORMACION SOBRE LA SIMETRIA DE LA DISTRIBUCION DE LOS DATOS, SI LA MEDIANA NO ESTA EN EL CENTRO DEL RECTANGULO LA DISTRIBUCION NO ES SIMETRICRA. #SON UTILES PARA VER LA PRESENCIA DE VALORES ATIPICOS O OUTLERS
boxplot(cafe_df$Rendimiento..ha.ton.,ylab="Rendimiento (ha/ton)")
boxplot(cafe_df$Produccion.Nacional..ton.,ylab="Produccion Nacional (ton)")
#SELECCIONAR POR RANGOS
boxplot(x=cafe_df$Rendimiento..ha.ton.[1:10],main="Rendimiento (ha/ton)")
#CON LA FUNCION HEXBIN SE PUEDE MEJORAR LA PRESENTACION Y LECTURA DE LOS GRAFICOS. #SE CREA EL OBJETO BIN CON LA RELACION DE DOS VARIABLES
bin1<-hexbin(cafe_df$Produccion..ton.,cafe_df$Rendimiento..ha.ton., xbins=10)## Warning in xy.coords(x, y, xl, yl): NAs introduced by coercionplot(bin1)
bin2<-hexbin(cafe_df$Area.Nacional..ha.,cafe_df$Rendimiento..ha.ton., xbins=10)
plot(bin2)
#HALLAMOS LA CORRELACION ENTRE LAS VARIABLES POR CORRELACION DE PEARSON- #INDICAMOS LAS VARIABLES CON VALORES DESCARTANDO LOS VALORES NULOS QUE EXISTAN
cor(cafe_df$Area.Nacional..ha.,cafe_df$Rendimiento..ha.ton., use="complete.obs")## [1] 0.2806767#PARA CALCULAR LA MODA SELECCIONANDO LAS VARIABLES EN EL DATAFRAME “cafe_df”
moda=function(var){
frec.var<-table(var)
valor=which(frec.var==max(frec.var)) #Elemento con el valor
names(valor)}
which.max(cafe_df$Rendimiento..ha.ton.)## [1] 121#SEGUIMOS ESTUDIANDO EL COMPORTAMIENTO O DISTRIBUCION DE LOS DATOS O LA INFORMACION CON GRAFICOS DE DISPERSION. #GRAFICO DE DISPERSION DEL COMPORTAMIENTO DE LA PRODUCCION VS RENDIMIENTO, PERMITE ENTENDER O DESCUBRIR PATRONES EN LOS DATOS
qplot(Produccion..ton., Rendimiento..ha.ton., data = cafe_df,
main ="Grafico de Dispersion",
col = "Red",
xlab ="Produccion (miles ton)",
ylab = "Rendimiento (ha/ton)")
#GRAFICO DE DISPERSION SEL COMPORTAMIENTO DE LA PRODUCCION NACIONAL VS RENDIMIENTO.
qplot(Produccion.Nacional..ton., Rendimiento..ha.ton., data = cafe_df,
main ="Grafico de Dispersion",
col = "Red",
xlab ="Produccion Nacional (miles ton)",
ylab = "Rendimiento (ha/ton)")
#COEFICIENTE DE CORRELACION DE PEARSON ENTRE LA PRODUCCION NACIONAL Y EL RENDIMIENTO. #HALLAS LA CORRELACION ENTRE LAS VARIABLES POR CORRELACION DE PEARSON #INDICAMOS QUE UTILIZA LAS VARIABLES CON VALORES DESCARTANDO LOS VALORES NULOS QUE EXISTAN
cor(cafe_df$Produccion.Nacional..ton.,cafe_df$Rendimiento..ha.ton., use="complete.obs")## [1] 0.3855697#GRAFICO DE DISPERSION DEL COMPORTAMIENTO ENTRE EL AREA NACIONAL VS EL RENDIMIENTO. PERMITE ENTENDER O DESCUBRIR PATRONES EN LOS DATOS
qplot(Area.Nacional..ha., Rendimiento..ha.ton., data = cafe_df,
main ="Grafico de Dispersion",
col = "Red",
xlab ="Area Nacional ha",
ylab = "Rendimiento (ha/ton)")
#COEFICIENTE DE CORRELACION DE PEARSON #COEFICIENTE DE CORRELACIÓN ENTRE EL AREA NA-CIONAL Y EL RENDIMIENTO #HALLAR LA CORRELACION ENTRE LAS VARIABLES POR METODO CORRELACION DE PEARSON #INDICAMOS QUE UTILIZA LAS VARIABLES CON VALORES DESCARTANDO LOS VALORES NULOS QUE EXISTAN
cor(cafe_df$Area.Nacional..ha.,cafe_df$Rendimiento..ha.ton., use="complete.obs")## [1] 0.2806767qplot(Anio, Rendimiento..ha.ton., data = cafe_df,
main ="Grafico de Dispersion",
col = "Red",
xlab ="Area Nacional ha",
ylab = "Rendimiento (ha/ton)")
#ESPECIFICAR EL DATAFRAME ES EL PRIMER ARGUMENTO EN LA FUNCIÓN GGPLOT # DENTRO DE AES() ESCRIBIMOS LAS VARIABLES (X,Y) QUE QUEREMOS GRAFICAR # PERMITE ENTENDER O DESCUBRIR PATRONES EN LOS DATOS # SE PUEDE UTILIZAR EL PARAMETRO COLOR PARA REPRESENTAR UNA TERCERA VARIABLE # GRÁFICO DE DISPERSION: AREA NACIONAL VS RENDIMIENTO
ggplot(cafe_df) +
geom_point(aes(x = Area.Nacional..ha., y = Rendimiento..ha.ton., color = Anio))
#GRAFICO DE LINEAS: AREA NACIONAL VS RENDIMIENTO
ggplot(cafe_df) +
geom_line(aes(x = Area.Nacional..ha., y = Rendimiento..ha.ton., color = Anio))
#GRAFICO DE DISPERSION: PRODUCCION NACIONAL VS RENDIMIENTO
ggplot(cafe_df) +
geom_point(aes(x = Produccion.Nacional..ton., y = Rendimiento..ha.ton., color = Anio))
#GRAFICO DE LINEAS: PRODUCCION NACIONAL VS RENDIMIENTO
ggplot(cafe_df) +
geom_line(aes(x = Produccion.Nacional..ton., y = Rendimiento..ha.ton., color = Anio))
#OTRA FORMA DE GRAFICO DE LINEAS SIN REPRESENTAR LOS AÑOS
data(cafe_df)## Warning in data(cafe_df): data set 'cafe_df' not foundggplot(cafe_df, aes(x = Produccion.Nacional..ton., y = Rendimiento..ha.ton., color ="red")) +
geom_line()
#HISTOGRAMA: AREA NACIONAL
ggplot(cafe_df) +
geom_histogram(aes(x = Area.Nacional..ha., color="red"))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#HISTOGRAMA PRODUCCION NACIONAL
ggplot(cafe_df) +
geom_histogram(aes(x = Produccion.Nacional..ton., color="red"))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#HISTOGRAMA RENDIMIENTO
ggplot(cafe_df) +
geom_histogram(aes(x = Rendimiento..ha.ton., color="red"))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#GRAFICO DE LINEAS: PRODUCCION NACIONAL VS RENDIMIENTO
ggplot(cafe_df) +
geom_line(aes(x = Produccion.Nacional..ton., y = Rendimiento..ha.ton., color = Anio))
#GRAFICO DE CAJAS: PRODUCCION NACIONA VS RENDIMIENTO
ggplot(cafe_df) +
geom_boxplot(aes(x = Produccion.Nacional..ton., y = Rendimiento..ha.ton., color = Anio))## Warning: Continuous x aesthetic -- did you forget aes(group=...)?
#GRAFICO DE CAJAS: AREA NACIONAS VS RENDIMIENTO
ggplot(cafe_df) +
geom_boxplot(aes(x = Area.Nacional..ha., y = Rendimiento..ha.ton., color = Anio))## Warning: Continuous x aesthetic -- did you forget aes(group=...)?
#GRAFICO DE DISPERSION JITTER: AREA NACIONAL VS RENDIMIENTO
ggplot(cafe_df) +
geom_jitter(aes(x = Area.Nacional..ha., y = Rendimiento..ha.ton., color = Anio))
#GRAFICO DE DISPERSION PRODUCCION NACIONAL VS RENDIMIENTO
ggplot(cafe_df) +
geom_point(aes(x = Produccion.Nacional..ton., y = Rendimiento..ha.ton., color = Anio))
#GRAFICO DE DISPERCION JITTER: PRODUCCION NACIONAL VS RENDIMIENTO
ggplot(cafe_df) +
geom_jitter(aes(x = Produccion.Nacional..ton., y = Rendimiento..ha.ton., color = Anio))
#OTRA FORMA CON GRAFICO DE DISPERSION SIN REPRESENTAR LOS AÑOS
data(cafe_df)## Warning in data(cafe_df): data set 'cafe_df' not foundggplot(cafe_df, aes(x = Produccion.Nacional..ton., y = Rendimiento..ha.ton., color ="red")) +
geom_point()
#OTRA FORMA CON GRAFICO DE LINEAS SIN REPRESENTAR LOS AÑOS
data(cafe_df)## Warning in data(cafe_df): data set 'cafe_df' not foundggplot(cafe_df, aes(x = Produccion.Nacional..ton., y = Rendimiento..ha.ton., color ="red")) +
geom_line()
#COMPARANDO CUANDO TIENE REPRESENTADO LOS AÑOS A CONTINUACION
ggplot(cafe_df, aes(x = Produccion.Nacional..ton., y = Rendimiento..ha.ton., color = Anio)) +
geom_line()
#TAMBIEN SE PUEDE REALIZAR DISPERSION EN PANELES POR AÑOS. #PERMITE ENTENDER O DESCUBRIR PATRONES EN LOS DATOS
ggplot(cafe_df, aes(x = Produccion.Nacional..ton., y = Rendimiento..ha.ton.)) +
geom_jitter() +
facet_grid(.~ Anio)
#PANELES SEPARADOS #TAMBIEN SE PUEDE REALIZAR DISPERSION EN PANELES SEPARADOS POR AÑOS
ggplot(cafe_df, aes(x = Produccion.Nacional..ton., y = Rendimiento..ha.ton.)) +
geom_point() +
facet_wrap(.~ Anio)
ggplot(cafe_df, aes(x = Area.Nacional..ha., y = Rendimiento..ha.ton.)) +
geom_point() +
facet_wrap(.~ Anio)
##COMPARANDO EL ANTERIOR CON EL GRAFICO DE DISPERSION SIN AÑOS # OTRA FORMA CON GRAFICO DE DISPERSION SIN REPRESENTAR LOS AÑOS
data(cafe_df)## Warning in data(cafe_df): data set 'cafe_df' not foundggplot(cafe_df, aes(x = Produccion.Nacional..ton., y = Rendimiento..ha.ton., color ="red")) +
geom_point()
#COMPARAR CON GRAFICO DE DISPERSION PRODUCCION NACIONAL VS RENDIMIENTO CON AÑOS
ggplot(cafe_df) +
geom_jitter(aes(x = Produccion.Nacional..ton., y = Rendimiento..ha.ton., color = Anio))
#AHORA SE UTILIZARA UN SUAVIZADOR #LA GRAFICA LINEAL SUAVIZADA
ggplot(cafe_df, aes(x = Area.Nacional..ha., y = Rendimiento..ha.ton.)) +
geom_point() +
facet_wrap(.~ Anio) +
geom_smooth(span = 3)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
FIN